In [1]:
#importing all the necessary libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
from imblearn .over_sampling import SMOTE

import sklearn
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score , classification_report , confusion_matrix , f1_score
In [2]:
#loading the dataset
df = pd.read_csv('C:\\Users\\Chandu\\Downloads\\diabetes.csv')
In [3]:
#viewing the dataset
df
Out[3]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
0 6 148 72 35 0 33.6 0.627 50 1
1 1 85 66 29 0 26.6 0.351 31 0
2 8 183 64 0 0 23.3 0.672 32 1
3 1 89 66 23 94 28.1 0.167 21 0
4 0 137 40 35 168 43.1 2.288 33 1
... ... ... ... ... ... ... ... ... ...
763 10 101 76 48 180 32.9 0.171 63 0
764 2 122 70 27 0 36.8 0.340 27 0
765 5 121 72 23 112 26.2 0.245 30 0
766 1 126 60 0 0 30.1 0.349 47 1
767 1 93 70 31 0 30.4 0.315 23 0

768 rows × 9 columns

In [4]:
#getting the dataset information
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 9 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Pregnancies               768 non-null    int64  
 1   Glucose                   768 non-null    int64  
 2   BloodPressure             768 non-null    int64  
 3   SkinThickness             768 non-null    int64  
 4   Insulin                   768 non-null    int64  
 5   BMI                       768 non-null    float64
 6   DiabetesPedigreeFunction  768 non-null    float64
 7   Age                       768 non-null    int64  
 8   Outcome                   768 non-null    int64  
dtypes: float64(2), int64(7)
memory usage: 54.1 KB
In [ ]:
In [5]:
df.isnull().sum()
#It tells that there are no null values.
Out[5]:
Pregnancies                 0
Glucose                     0
BloodPressure               0
SkinThickness               0
Insulin                     0
BMI                         0
DiabetesPedigreeFunction    0
Age                         0
Outcome                     0
dtype: int64
In [6]:
#viewing the statistical information
df.describe()
Out[6]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
count 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000
mean 3.845052 120.894531 69.105469 20.536458 79.799479 31.992578 0.471876 33.240885 0.348958
std 3.369578 31.972618 19.355807 15.952218 115.244002 7.884160 0.331329 11.760232 0.476951
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.078000 21.000000 0.000000
25% 1.000000 99.000000 62.000000 0.000000 0.000000 27.300000 0.243750 24.000000 0.000000
50% 3.000000 117.000000 72.000000 23.000000 30.500000 32.000000 0.372500 29.000000 0.000000
75% 6.000000 140.250000 80.000000 32.000000 127.250000 36.600000 0.626250 41.000000 1.000000
max 17.000000 199.000000 122.000000 99.000000 846.000000 67.100000 2.420000 81.000000 1.000000
In [7]:
 #The min value for "Glucose , BloodPressure , SkinThickness and BMI" is 0 which is not logic value for such features so we should deal with it , by Replacing the 0 value with null.
df.loc[:,'Glucose'].replace(0 , np.NaN , inplace=True)
df.loc[:,'BloodPressure'].replace(0 , np.NaN , inplace=True)
df.loc[:,'SkinThickness'].replace(0 , np.NaN , inplace=True)
df.loc[:,'BMI'].replace(0 , np.NaN , inplace=True)
In [8]:
#viewing the statistical information
df.describe()
Out[8]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
count 768.000000 763.000000 733.000000 541.000000 768.000000 757.000000 768.000000 768.000000 768.000000
mean 3.845052 121.686763 72.405184 29.153420 79.799479 32.457464 0.471876 33.240885 0.348958
std 3.369578 30.535641 12.382158 10.476982 115.244002 6.924988 0.331329 11.760232 0.476951
min 0.000000 44.000000 24.000000 7.000000 0.000000 18.200000 0.078000 21.000000 0.000000
25% 1.000000 99.000000 64.000000 22.000000 0.000000 27.500000 0.243750 24.000000 0.000000
50% 3.000000 117.000000 72.000000 29.000000 30.500000 32.300000 0.372500 29.000000 0.000000
75% 6.000000 141.000000 80.000000 36.000000 127.250000 36.600000 0.626250 41.000000 1.000000
max 17.000000 199.000000 122.000000 99.000000 846.000000 67.100000 2.420000 81.000000 1.000000
In [9]:
df.head(10)
Out[9]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
0 6 148.0 72.0 35.0 0 33.6 0.627 50 1
1 1 85.0 66.0 29.0 0 26.6 0.351 31 0
2 8 183.0 64.0 NaN 0 23.3 0.672 32 1
3 1 89.0 66.0 23.0 94 28.1 0.167 21 0
4 0 137.0 40.0 35.0 168 43.1 2.288 33 1
5 5 116.0 74.0 NaN 0 25.6 0.201 30 0
6 3 78.0 50.0 32.0 88 31.0 0.248 26 1
7 10 115.0 NaN NaN 0 35.3 0.134 29 0
8 2 197.0 70.0 45.0 543 30.5 0.158 53 1
9 8 125.0 96.0 NaN 0 NaN 0.232 54 1
In [10]:
#checking the null values again
df.isnull().sum()
Out[10]:
Pregnancies                   0
Glucose                       5
BloodPressure                35
SkinThickness               227
Insulin                       0
BMI                          11
DiabetesPedigreeFunction      0
Age                           0
Outcome                       0
dtype: int64
In [11]:
#viewing the heatmap for the null values
plt.figure(figsize=(10,5))
sns.heatmap(df.isna() , cmap='Blues')
plt.show()
In [12]:
#dealing with the null values: replacing the null values with the mean value
df.loc[:,'Glucose'].fillna(df.loc[:,'Glucose'].mean(),inplace=True)
df.loc[:,'BloodPressure'].fillna(df.loc[:,'BloodPressure'].mean(),inplace=True)
df.loc[:,'SkinThickness'].fillna(df.loc[:,'SkinThickness'].mean(),inplace=True)
df.loc[:,'BMI'].fillna(df.loc[:,'BMI'].mean(),inplace=True)
In [13]:
df
Out[13]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
0 6 148.0 72.0 35.00000 0 33.6 0.627 50 1
1 1 85.0 66.0 29.00000 0 26.6 0.351 31 0
2 8 183.0 64.0 29.15342 0 23.3 0.672 32 1
3 1 89.0 66.0 23.00000 94 28.1 0.167 21 0
4 0 137.0 40.0 35.00000 168 43.1 2.288 33 1
... ... ... ... ... ... ... ... ... ...
763 10 101.0 76.0 48.00000 180 32.9 0.171 63 0
764 2 122.0 70.0 27.00000 0 36.8 0.340 27 0
765 5 121.0 72.0 23.00000 112 26.2 0.245 30 0
766 1 126.0 60.0 29.15342 0 30.1 0.349 47 1
767 1 93.0 70.0 31.00000 0 30.4 0.315 23 0

768 rows × 9 columns

In [14]:
df.isnull().sum()
Out[14]:
Pregnancies                 0
Glucose                     0
BloodPressure               0
SkinThickness               0
Insulin                     0
BMI                         0
DiabetesPedigreeFunction    0
Age                         0
Outcome                     0
dtype: int64
In [15]:
df.describe()
Out[15]:
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
count 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000
mean 3.845052 121.686763 72.405184 29.153420 79.799479 32.457464 0.471876 33.240885 0.348958
std 3.369578 30.435949 12.096346 8.790942 115.244002 6.875151 0.331329 11.760232 0.476951
min 0.000000 44.000000 24.000000 7.000000 0.000000 18.200000 0.078000 21.000000 0.000000
25% 1.000000 99.750000 64.000000 25.000000 0.000000 27.500000 0.243750 24.000000 0.000000
50% 3.000000 117.000000 72.202592 29.153420 30.500000 32.400000 0.372500 29.000000 0.000000
75% 6.000000 140.250000 80.000000 32.000000 127.250000 36.600000 0.626250 41.000000 1.000000
max 17.000000 199.000000 122.000000 99.000000 846.000000 67.100000 2.420000 81.000000 1.000000
In [16]:
df['Outcome'].value_counts()
Out[16]:
0    500
1    268
Name: Outcome, dtype: int64
In [17]:
sns.countplot(data = df , x='Outcome')
Out[17]:
<AxesSubplot:xlabel='Outcome', ylabel='count'>
In [18]:
plt.figure(figsize=(10,5))
col = ['non diabetics','diabetics']
px.pie(df , values=df['Outcome'].value_counts(),names=col)

<Figure size 720x360 with 0 Axes>
In [19]:
#this tells that our dataset has more non-diabetic patients than diabetic.
In [20]:
#dividing the dataset into x(input) and y(output) features
x = df.drop('Outcome',axis=1).values
y = df['Outcome'].values
In [21]:
print(x)

[[  6.    148.     72.    ...  33.6     0.627  50.   ]
 [  1.     85.     66.    ...  26.6     0.351  31.   ]
 [  8.    183.     64.    ...  23.3     0.672  32.   ]
 ...
 [  5.    121.     72.    ...  26.2     0.245  30.   ]
 [  1.    126.     60.    ...  30.1     0.349  47.   ]
 [  1.     93.     70.    ...  30.4     0.315  23.   ]]
In [22]:
print(y)

[1 0 1 0 1 0 1 0 1 1 0 1 0 1 1 1 1 1 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0
 1 1 1 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0
 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1
 1 0 0 1 1 1 0 0 0 1 0 0 0 1 1 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 1 0 1 0 1 0 0 0 0 0
 1 1 1 1 1 0 0 1 1 0 1 0 1 1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 1 1 1 1 0 1 1 1 1
 0 0 0 0 0 1 0 0 1 1 0 0 0 1 1 1 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0
 1 0 1 0 0 1 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 1 1 0 0
 1 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 0 1 0 0 1 0 1 0 1 1 1 0 0 1 0 1 0 0 0 1
 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 1 1 0 0 1 0 0 1 0 0 1
 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 1 0 0 1 0 1 1 0 1 0 1 0 1
 0 1 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1
 1 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1
 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0
 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0
 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0 1 0 1 0 1 0
 1 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 1 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0
 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 1 1 1 1 0
 1 1 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 1 1
 0 0 0 1 0 1 1 0 0 1 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1
 1 0 0 1 0 0 1 0 1 1 1 0 0 1 1 1 0 1 0 1 0 1 0 0 0 0 1 0]
In [23]:
#preprocessing the dataset
X_scaler = StandardScaler().fit_transform(x)
In [24]:
#splitting the dataset into testing and training part
x_train, x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)
In [25]:
#viewing the dataset shape
x_train.shape
Out[25]:
(614, 8)
In [26]:
y_train.shape
Out[26]:
(614,)
In [27]:
x_test.shape
Out[27]:
(154, 8)
In [28]:
y_test.shape
Out[28]:
(154,)
In [34]:
#training our model using KNN
knn = KNeighborsClassifier(n_neighbors=10).fit(x_train , y_train)
knn.score(x_train , y_train)*100
Out[34]:
79.15309446254072
In [43]:
#predicting the output values from x_testing data part
y_pred = knn.predict(x_test)
y_pred
Out[43]:
array([0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
       1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0,
       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
       0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1,
       0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,
       0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
      dtype=int64)
In [36]:
#viewing the y_testing(output) datapart
y_test
Out[36]:
array([0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0,
       1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,
       1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1,
       0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1,
       0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,
       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0],
      dtype=int64)
In [37]:
#difference between the actual and predicted output values
y_test - y_pred
Out[37]:
array([ 0,  0,  0,  1,  0,  0,  0, -1,  0,  0,  0, -1,  1,  0,  0,  1,  0,
        0,  0,  0,  1,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0, -1,  0,
       -1,  0,  0,  0,  1,  0,  0,  1,  0,  0,  1,  0,  0,  1,  0,  0,  1,
        0,  0,  1,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  1,  1,  0,  1,
        0,  1,  0,  0,  1,  0,  1,  0, -1, -1,  0,  1,  0, -1,  0,  0,  0,
        0,  0,  0,  0,  1, -1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  1,  0, -1,  0,  0,  0,  0, -1,  0,  0,  1, -1,  0,  1,
        0,  1,  0,  0,  0,  0,  0, -1,  0,  0,  1,  0,  0,  0,  0,  0, -1,
        0,  0,  0,  1,  0,  0,  0,  0,  1,  1,  0,  0,  1,  0,  0, -1,  0,
        0], dtype=int64)
In [38]:
#checking the accuracy score
print(accuracy_score(y_true=y_test , y_pred=y_pred)*100)

72.07792207792207
In [45]:
#Note our data set is imbalnce , so the best performance evaluation is f1_score
target_name = []
classification_report(y_true=y_test , y_pred=y_pred,output_dict=True)
Out[45]:
{'0': {'precision': 0.7433628318584071,
  'recall': 0.8571428571428571,
  'f1-score': 0.7962085308056872,
  'support': 98},
 '1': {'precision': 0.6585365853658537,
  'recall': 0.48214285714285715,
  'f1-score': 0.5567010309278351,
  'support': 56},
 'accuracy': 0.7207792207792207,
 'macro avg': {'precision': 0.7009497086121304,
  'recall': 0.6696428571428571,
  'f1-score': 0.6764547808667611,
  'support': 154},
 'weighted avg': {'precision': 0.7125169240429332,
  'recall': 0.7207792207792207,
  'f1-score': 0.7091148944864683,
  'support': 154}}
In [ ]:
#Hence, this model gives 72% accuracy score
In [ ]: